{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:58:20Z","timestamp":1773511100267,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164392","type":"print"},{"value":"9783031164408","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_7","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"68-78","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Trichomonas Vaginalis Segmentation in\u00a0Microscope Images"],"prefix":"10.1007","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xunkun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian-Zhu","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Brandao, P., Mazomenos, E., Ciuti, G., Cali\u00f2, R., Bianchi, F., Menciassi, A., et al.: Fully convolutional neural networks for polyp segmentation in colonoscopy. In: Medical Imaging: Computer-Aided Diagnosis. vol. 10134, pp. 101\u2013107 (2017)","DOI":"10.1117\/12.2254361"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: IEEE CVPR, pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: IEEE ICCV, pp. 4548\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.487"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. In: IJCAI. pp. 698\u2013704 (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Cheng, M.M., Shao, L.: Concealed object detection. IEEE TPAMI, pp. 1 (2021)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: IEEE CVPR, pp. 2777\u20132787 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Fan, D.P., et al.: Pranet: parallel reverse attention network for polyp segmentation. In: MICCAI, pp. 263\u2013273 (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"issue":"8","key":"7_CR8","first-page":"2626","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan, D.P., Zhou, T., Ji, G.P., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.: INF-NET: Automatic covid-19 lung infection segmentation from CT images. IEEE TMI 39(8), 2626\u20132637 (2020)","journal-title":"IEEE TMI"},{"issue":"2","key":"7_CR9","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"SH Gao","year":"2019","unstructured":"Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE TPAMI 43(2), 652\u2013662 (2019)","journal-title":"IEEE TPAMI"},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ejogrb.2011.02.024","volume":"157","author":"DF Harp","year":"2011","unstructured":"Harp, D.F., Chowdhury, I.: Trichomoniasis: evaluation to execution. Eur. J. Obstet. Gynecol. Reprod. Biol. 157(1), 3\u20139 (2011)","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., Davy, A., Warde-Farley, D., Biard, A., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"issue":"4","key":"7_CR12","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582\u2013596 (2019)","journal-title":"J. Digit. Imaging"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., Wu, J.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP. pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"7_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-87193-2_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"G-P Ji","year":"2021","unstructured":"Ji, G.-P., Chou, Y.-C., Fan, D.-P., Chen, G., Fu, H., Jha, D., Shao, L.: Progressively normalized self-attention network for video polyp segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. Progressively normalized self-attention network for video polyp segmentation, vol. 12901, pp. 142\u2013152. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_14"},{"key":"7_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Li, D., et al.: Robust blood cell image segmentation method based on neural ordinary differential equations. In: Computational and Mathematical Methods in Medicine 2021 (2021)","DOI":"10.1155\/2021\/5590180"},{"key":"7_CR17","unstructured":"Li, J., et al.: A systematic collection of medical image datasets for deep learning. arXiv preprint arXiv:2106.12864 (2021)"},{"issue":"1","key":"7_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.tbench.2021.100008","volume":"1","author":"L Li","year":"2021","unstructured":"Li, L., Liu, J., Yu, F., Wang, X., Xiang, T.Z.: Mvdi25k: A large-scale dataset of microscopic vaginal discharge images. BenchCouncil Transactions on Benchmarks, Standards and Evaluations 1(1), 100008 (2021)","journal-title":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations"},{"key":"7_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102205","volume":"74","author":"J Liu","year":"2021","unstructured":"Liu, J., Dong, B., Wang, S., Cui, H., Fan, D.P., Ma, J., Chen, G.: Covid-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework. Med. Image Anal. 74, 102205 (2021)","journal-title":"Med. Image Anal."},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: IEEE CVPR. pp. 248\u2013255 (2014)","DOI":"10.1109\/CVPR.2014.39"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE CVPR, pp. 733\u2013740. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"7_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404","volume":"106","author":"X Qin","year":"2020","unstructured":"Qin, X., Zhang, Z., Huang, C., Dehghan, M., et al.: U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)","journal-title":"Pattern Recogn."},{"key":"7_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. U-net: Convolutional networks for biomedical image segmentation, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access, pp. 82031\u201382057 (2021)","DOI":"10.1109\/ACCESS.2021.3086020"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, W., Wang, H., Li, S., Li, X.: Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. In: IEEE CVPR, pp. 1407\u20131417 (2021)","DOI":"10.1109\/CVPR46437.2021.00146"},{"issue":"11","key":"7_CR26","doi-asserted-by":"publisher","first-page":"6769","DOI":"10.1007\/s00521-019-04700-0","volume":"32","author":"W Tang","year":"2020","unstructured":"Tang, W., Zou, D., Yang, S., Shi, J., Dan, J., Song, G.: A two-stage approach for automatic liver segmentation with faster R-CNN and deeplab. Neural Comput. Appl. 32(11), 6769\u20136778 (2020)","journal-title":"Neural Comput. Appl."},{"issue":"10053","key":"7_CR27","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1016\/S0140-6736(16)31678-6","volume":"388","author":"T Vos","year":"2016","unstructured":"Vos, T., Allen, C., Arora, M., Barber, R.M., Bhutta, Z.A., Brown, A., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990\u20132015: a systematic analysis for the global burden of disease study 2015. The Lancet 388(10053), 1545\u20131602 (2016)","journal-title":"The Lancet"},{"issue":"6","key":"7_CR28","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.3390\/app11062738","volume":"11","author":"X Wang","year":"2021","unstructured":"Wang, X., Du, X., Liu, L., Ni, G., Zhang, J., Liu, J., Liu, Y.: Trichomonas vaginalis detection using two convolutional neural networks with encoder-decoder architecture. Appl. Sci. 11(6), 2738 (2021)","journal-title":"Appl. Sci."},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: MICCAI. pp. 699\u2013708 (2021)","DOI":"10.1007\/978-3-030-87193-2_66"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, S., Huang, Q.: F$$^3$$net: fusion, feedback and focus for salient object detection. In: AAAI, pp. 12321\u201312328 (2020)","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: ECCV. pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"1","key":"7_CR32","first-page":"11","volume":"20","author":"KA Workowski","year":"2012","unstructured":"Workowski, K.A.: Sexually transmitted infections and HIV: diagnosis and treatment. Topics Antiviral Med. 20(1), 11 (2012)","journal-title":"Topics Antiviral Med."},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: IEEE CVPR, pp. 3907\u20133916 (2019)","DOI":"10.1109\/CVPR.2019.00403"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. In: IEEE ICCV, pp. 7263\u20137272 (2019)","DOI":"10.1109\/ICCV.2019.00736"},{"key":"7_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/978-3-030-87193-2_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: A multi-branch hybrid transformer network for corneal endothelial cell segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 99\u2013108. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_10"},{"key":"7_CR36","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Wu, Y., Song, G., Li, Z., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98\u2013111 (2018)","journal-title":"Med. Image Anal."},{"key":"7_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-87193-2_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Zhao","year":"2021","unstructured":"Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120\u2013130. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_12"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: DLMIA, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE TMI, pp. 1856\u20131867 (2019)","DOI":"10.1109\/TMI.2019.2959609"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:07:14Z","timestamp":1711562834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}